The future of embedded analytics

Evolving from native dashboards to automated decisions

By Alison Bolen, SAS Insights Editor

Embedded analytics go hand-in-hand with big data and the Internet of Things (IoT). Just as embedded sensors spawned the IoT revolution and embedded processors helped create the big data revolution, embedded analytics will usher in a new era of even smarter devices.

Let’s take a closer look at what we mean by embedded analytics and how the term has evolved.

Even five years ago, when we talked about embedded analytics, we were referring to the ability to access dashboards and other reports directly inside an operational system. Fulfillment systems, resource planning tools and customer support platforms all started including their own analytics dashboards so you could look up information about inventories, dropped calls and turnaround times without having to open a separate program.

Around that same time, in popular culture, we were using the word “embed” to describe things like:

Embedding videos into blog posts.

Embedding journalists into military units.

Embedding spreadsheets into our written reports.

In all of these situations, the item being embedded maintains its existing format or purpose even when it is transplanted into a new environment.

Analytics five years ago was primarily delivered to decision makers in dashboards or reports, so embedding it into other systems meant making dashboards and reports available natively in those systems.

It seemed like a big deal at the time to get your hands on some simple reports without having to leave your favorite program and go look for the data someplace else.

Analytics permeates our lives

Today, however, analytics is everywhere. It isn’t just delivered in charts and graphs. Instead, analytics is working behind the scenes on our phones, in our cars, on the web and inside transactional systems.

Likewise, analytics is no longer just provided to humans for individual decisions. It is embedded right into the activity of our systems so that actions can be taken without our intervention.

You swipe your credit card, and the sales associate knows immediately if the card has been flagged for suspicious activity. You sign up for a clinical trial, and your phone analyzes daily inputs about your health without any direct data entry from you. You purchase an item online, and your social media apps know to up-sell related items the next time you log on.

These are examples of where we see embedded analytics now, but where do we expect to see these technologies evolve in the future? With the power of streaming analytics and advanced API technologies that help programs talk to each other, the next wave of embedded analytics will be able to trigger decisions from one device to another.

According to recent TDWI research, embedding analytics into devices is set to grow. “As analytics becomes operationalized and embedded,” writes Fern Halper in Operationalizing and Embedding Analytics for Action, “it becomes more pervasive … it may also become more programmatic and closer to real time.”

As we add sensors to more and more things and embed analytics into the fabric of those things, it’s not just a matter of making the individual things smarter, but making the entire network of systems and machines work smarter collectively.

At home, many daily tasks that are manual will become automated. And at work, individual systems – from design and production to inventory and delivery – will be networked together to automatically make immediate decisions about very complex processes.

Roadways could reroute drivers based on an anticipated traffic jam. Manufacturing plants could reschedule maintenance plans to optimize production. Energy providers could trigger corrective actions in the smart grid to maintain grid reliability. And it will all happen automatically as part of regular operations.

The Internet of Things redefines how we engage with the physical world and makes computer-mediated ways of doing business possible, from managing public infrastructure to organizing people’s lives.

As we add sensors to more and more things and embed analytics into the fabric of those things, it’s not just a matter of making the individual things smarter, but making the entire network of systems and machines work smarter collectively.

The challenges and opportunities of embedded analytics

As our workplaces and our homes continue to flourish with sensors, and as the amount of data flowing from device to device grows, the challenges for analytics also evolve. Suddenly, the problem shifts from information gathering to information overload.

Embedding analytics correctly into operational processes takes this into account by pulling out and analyzing only the data needed for making the most important decisions right now.

Imagine a shirt or device that tracks the heart rate and blood pressure of a friend with a heart condition. Those metrics are interesting. But what if the wearable device could analyze all of her vital signs, predict that she’s likely to have a heart attack and call an ambulance? That’s useful. Embedded analytics can shift technology from an attractive gadget to a potential lifesaving device.

Moving from interesting to useful is only possible by knowing what data is important and what data can be ignored or stored for later. In many cases, obvious rules can be set up to trigger decisions. But also – as systems become more complex and more data flows through the network – embedded analytics will rely on self-learning models and adapt decisions based on the data that comes in.

Self-driving cars will adapt to different weather conditions based on past experiences. Fraud detection systems will identify fraud rings based on familiar patterns in a network of connected users – and take immediate action, like blocking a money transfer.

Recommendation systems on the web that suggest items based on previous behaviors are a classic example of embedded analytics. One way to find opportunities for embedded analytics in your world is to extend the idea of recommendation systems into other areas of your life – and then move beyond recommendations to taking action. What ideas can you come up with? And how can you use analytics to make them happen?